A fusion method for capacity estimation of fast-charging lithium-ion battery under varied degradation mode

Recently, lithium-ion batteries with fast-charging capability have been gradually equipped in electric products. Capacity estimation specially developed for fast-charging batteries is still an open question, and most of the existing works are proposed for batteries charged under the 1C rate. However, incorrect fast-charging strategies may result in Li plating leading to battery capacity aging rapidly and degradation mode variation. Moreover, unprecise sampling by BMS increases the difficulty of accurately estimating battery capacity in such scenarios. Thus, a fusion prognostic method based on ensemble learning is proposed for the above issues in fast-charging batteries. Firstly, measurement-based health features are extracted from the portion charging phase. Then, the accuracy of the validation dataset-based strategy is proposed to achieve time-varying weight allocation. Finally, a fusion prognostic model is constructed based on the above weight allocation strategy and health features. The effectiveness of the proposed fusion method is verified by a self-design fast-charging battery dataset that maintains stable and reliable performance under sparse sampling and degradation mode variation conditions. In addition, the robustness and adaptability are validated by the comparison experiments with traditional ensemble learning-based methods, in which accuracy is improved by 27.5% minimally.